The modernization of legacy industrial equipment through smart retrofitting is crucial for enhancing operational efficiency, reducing maintenance costs, and ensuring the long-term viability of production assets. Many existing retrofitting approaches focus on isolated technical solutions without providing a structured and transferable methodology applicable to various industrial environments. This paper presents a holistic smart retrofit process that integrates predictive maintenance (PdM) principles, sensor deployment, and data-driven decision-making to extend the lifespan of aging machinery while improving reliability and maintainability. By systematically identifying critical components, optimizing sensor placement, and establishing a standardized integration framework, the proposed methodology increases the predictability of complex technical systems, enables better utilization of components, and supports secure maintenance planning through real-time condition monitoring and failure forecasting. This work advances the digital transformation of legacy machinery, providing a scalable and efficient approach that aligns with Industry 4.0 principles and facilitates seamless integration into modern industrial ecosystems.